Machine learning is a fast-growing field. Many different algorithms are used in machine learning today. I present to you a new algorithm that is "LightGBM" because it is a new algorithm and there are not many resources to understand the algorithm. In this blog, I will try to be specific and keep the blog small and explain to you how you can use the LightGBM algorithm for different machine learning tasks. If you go through the LightGBM documentation, you will see that there are a large number of parameters provided and one can easily be confused about using the parameter. I will try to make these things easier for you.
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WHAT IS LIGHTGBM?
It is a gradient enhancement framework that uses tree-based learning algorithms, which is considered a very powerful algorithm when it comes to computations. It is considered a fast processing algorithm.
While some algorithm trees grow horizontally, the LightGBM algorithm grows vertically, which means that the tab grows and other algorithms grow one level up. LightGBM selects the leaf with the largest loss for growth. This reduces additional losses as a level-level algorithm as the same sheet grows.
WHY IS LIGHTGBM SO POPULAR?
For traditional algorithms, it is difficult to produce fast outcomes because the size of the data increases rapidly every day. LightGBM is called "Light" because of its ability to count faster and deliver results. It requires little memory to run and can process large amounts of data. The most used algorithm of Hackathon is because the motive of the algorithm is to obtain good accuracy of results and also to create GPU reliability.
WHAT ARE THE PARAMETERS OF LIGHTGBM?
It is very important to know the basic parameters of the algorithm you are using. LightGBM has provided more than 100 parameters in the LightGBM documentation, but it is not necessary to study them all. Let's see what the different parameters are.
Control Parameters
- Maximum depth: This adds depth to the wood and also controls for excessive model placement. If you feel that your model is growing below the maximum depth.
- Min_data_in_leaf: The minimum number of sheet records that are also used to override the control model. Feature_fraction: This specifies a randomly selected parameter each time the tree building returns. If 0.7, it means that 70% of the parameter is used.
- Bagging_fraction: Checks the fraction of data used on each return trip. It is often used to increase training speed and prevent overeating.
- Early_stopping_round: If the metric validation data shows some improvement in the last rounds of early_stopping_round. This will reduce bad returns.
- Lambda: It means regularization. Values range from 0 to 1.
- Min_gain_to_split: Used to control the number of splits in the tree.
Main Parameter
- Task: Tells about the task that needs to be performed with the data. It can be data training as a data prediction.
- Application: This parameter determines whether a change or classification should be made. The default LightGBM parameter for the application is regression.
- Binary: Used for binary classification.
- Multiclass: Used for multiclass problems. Regression: Used to perform regression.
- Improvement: This determines the type of algorithm.
- rf: Used for Random Forest.
- Cast: Transition-based side sampling.
- Num_boost_round: This talks about increasing returns. Learning_rate: The function of learning speed is to take into account the range of changes in the estimate that will be updated from the output of each tree. It has values: 0.1, 0.001, 0.003.
- Num_leaves: Specifies the total number of leaves present in the tree, default: 31
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Measurement of Parameters
Causes a loss when building a model. Some of them are listed below for classification as returns.
- Mae: Explain it completely wrong. Me: Mean square error.
- Binary_logloss: Loss in binary classification.
- Multi_logloss: Multiple Logloss Loss.
Tuning Parameters
Parameter tuning is an important part often performed by data scientists to achieve good accuracy, fast results, and deal with reassembly. Let's then show you some parameter tuning that you can do for better results. num_leaves: This parameter is responsible for the complexity of the model. Its values must be less than or equal to 2. If the value is greater, this will lead to an excessive adjustment of the network model.
If you need to speed things up faster:
- Taw small values to max_bin.
- Use bagging according to bagging fraction and bagging frequency.
- Set feature_fraction to use the sub-sampling function.
- Use save_binary to make loading data easier in the future. If you want good accuracy:
- For large amounts of num_itration, use a low learning speed.
- Enter the maximum value of max_bin.
- Enter a large number of num_leaves.
- Your training data should be larger.
- Use direct categorical functions. If you want to deal with resetting the model
- Enter the small values of max_bin and num_leaves.
- Use a large amount of training data.
- Use max_depth to avoid deep trees.
- Use bagging by setting bagging_fraction and bagging_freq.
- Set feature_fraction to use the sub-sampling function. Use l1 and l2 & min_gain_to_split for regularisation
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